Best Open Source LLM
2026 Live Benchmark Ranking
The definitive ranking of open-weight AI models you can self-host, fine-tune, and deploy without restrictions. This page now uses the same live dataset and open-source filtering logic as the Explore leaderboard, so the ordering matches the table view across the app.
Top 3 Open Source Models
Full Open Source LLM Rankings 2026
| Rank | Model | Quality | Best Price | Top Speed | Max Context | Providers |
|---|---|---|---|---|---|---|
| 1 | Kimi K3.1 Pro Kimi | 46.5 | $3.60/M | 258 tok/s | 1M | 16 |
| 2 | Muse Wonder Meta | 43.6 | $8.00/M | 120 tok/s | 1M | 1 |
| 3 | Muse Wonder (High) Meta | 40.5 | $8.00/M | 120 tok/s | 1M | 1 |
| 4 | Muse Spark 1.3 Meta | 36.6 | $2.00/M | 127 tok/s | 1M | 1 |
| 5 | Muse Ember Meta | 36 | $0.20/M | 454 tok/s | 1M | 1 |
| 6 | Muse Ember (Xhigh) Meta | 34.1 | $0.20/M | 459 tok/s | 1M | 1 |
| 7 | Qwen3.8 Max Alibaba | 33.7 | $3.00/M | 53 tok/s | 984K | 1 |
| 8 | DeepSeek V4.1 Flash 1017 DeepSeek | 32.6 | $0.15/M | 860 tok/s | 1M | 21 |
| 9 | GLM-5.3 Z AI | 31.7 | $0.31/M | 348 tok/s | 1M | 24 |
| 10 | MiMo-V2.6-Pro Xiaomi | 29.9 | $0.41/M | 163 tok/s | 1M | 4 |
| 11 | Ling 3.1 Flash 1019 InclusionAI | 29.6 | $0.45/M | 0 tok/s | 1M | 1 |
| 12 | MiniMax-M3.1-Flash MiniMax | 29.1 | $0.20/M | 402 tok/s | 1M | 12 |
Run Open-Weight LLMs Locally
Hardware is the constraint that turns a benchmark winner into a practical local model. Use these tiers as a starting point, then open the dedicated Ollama guide for commands and artifact sizes:
8GB VRAM
RTX 3070 · M2 MacBook Air
- → Qwen3 8B (code + chat)
- → Gemma 4 12B (multimodal)
- → Smaller Qwen3 variants (fast)
16–24GB VRAM
RTX 3090/4090 · M2 Pro/Max
- → Qwen3-Coder 30B-A3B (coding)
- → Qwen3.6 27B (balanced)
- → Gemma 4 26B (general)
48GB+ VRAM
2× RTX 4090 · Mac Studio M2 Ultra
- → Qwen3-Coder-Next (local coding)
- → Qwen3-Next (efficient MoE)
- → Larger current models after size checks
Tip: artifact size is not total runtime memory. Leave room for the context cache, serving framework, operating system, and other applications. The official Qwen3-Coder 30B Ollama artifact is about 19GB; Qwen3-Coder-Next Q4 is about 52GB.
Which Open Source LLM Should You Use?
By Use Case
- →Coding: Kimi K3.1 Pro currently leads the open-source coding subset on this page
- →Best overall quality: Kimi K3.1 Pro is the strongest open-weight pick in the live ranking
- →Fast responses: DeepSeek V4.1 Flash 1017 has the best top-end output speed among the current open-source leaders
- →Long documents: Kimi K3.1 Pro offers the largest context window in this live ranking
By Deployment
- →Cheap API: DeepSeek V4.1 Flash 1017 is currently the cheapest high-ranking option at $0.15/M
- →Widest availability: GLM-5.3 has the most provider coverage in the current leaderboard
- →Self-host / Ollama: Use the live ranking here as a shortlist, then jump into the dedicated local and Ollama guides for hardware-specific picks
- →Commercial use: Verify the exact license for the model family you choose before shipping it into production
How This Ranking Works
Only models with openly available weights are included. This page uses the same grouped Explore dataset and open-source filter as the live leaderboard, then orders those model families by Artificial Analysis Quality Index. We still surface coding benchmarks here when they are available:
MMLU-Pro
Comprehensive knowledge benchmark across 14 domains. Tests breadth of model capability.
AIME 2025
Competition math — tests advanced reasoning. Best signal for math and science tasks.
LiveCodeBench
Contamination-free code generation. Best signal for software development capability.
Compare Open Source Models Side by Side
See live pricing from self-hosting providers, latency, and full benchmark scores for all open source models.
Frequently Asked Questions
What is the best open source LLM in 2026?
Kimi K3.1 Pro leads the live open-source ranking with a Quality Index of 46.5. For price-sensitive deployments, DeepSeek V4.1 Flash 1017 is the current budget leader at $0.15/M.
How do I choose an open-weight model to run locally?
Start with the hardware you have, then narrow by task. while Kimi K3.1 Pro is better when context size matters most.
Can open source LLMs match GPT-5 or Claude in 2026?
For most tasks, yes. The top open source models in 2026 trail proprietary leaders by only a small Quality Index margin. The main gaps remain in instruction-following polish, multimodal capability, and very long contexts.
What hardware do I need to run LLMs locally?
As a rule of thumb, 8GB VRAM is enough for smaller 7B to 8B models, 24GB VRAM is a more practical floor for 30B-class models, and 40GB+ is usually required once you move into 70B territory unless you quantize aggressively. Apple Silicon is also viable for smaller and mid-sized open-weight models when unified memory is high enough.
Which open source model is best for coding?
Kimi K3.1 Pro is the strongest coding-oriented open source model on this page. For cheap API access, DeepSeek V4.1 Flash 1017 is the most economical current pick. See the full coding LLM ranking for more detail.
Related Rankings
Best for Coding
All models ranked
🧮Math & Reasoning
AIME 2025 rankings
🖥️Best Local LLM
Self-hosted picks
🦙Best Ollama Models
Ollama-first picks
📄Long Context
Best for documents
⚡Side-by-Side
Compare any 2–4 models
Data sources: Rankings use the same live Explore dataset and open-source filter as the methodology page describes. Explore the filtered table in our interactive leaderboard or compare models side by side.